Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”
Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.
Enterprise GenAI 2024: applications will likely surge toward production, according to Snorkel AI Enterprise LLM Summit survey results .
Training large language models is a multi-layered stack of processes, each with its unique role and contribution to the model’s performance.
Low-rank adaptation (LoRA) lets data scientists customize GenAI models like LLMs faster than traditional full fine-tuning methods.
LLM distillation isolates task-specific LLM performance and mirrors it in a smaller format—creating faster and cheaper performance.
Snorkel AI CEO Alex Ratner explains his view on the importance of AI in data development and illustrates his position with two case studies.
Snorkel CEO Alex Ratner talks with QBE Ventures’ Alex Taylor about the future of AI, LLMs and multimodal models in the insurance industry.
We’ve developed new approaches to scale human preferences and align LLM output to enterprise users’ expectations by magnifying SME impact.
